Man-made object classification in SAR images using 2-D cepstrum
Author
Eryildirim, A.
Çetin, A. Enis
Date
2009-05Source Title
IEEE National Radar Conference - Proceedings
Publisher
IEEE
Pages
[1] - [4]
Language
English
Type
Conference PaperItem Usage Stats
146
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115
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Abstract
In this paper, a novel descriptive feature parameter extraction method from Synthetic Aperture Radar (SAR) images is proposed. The new method is based on the two-dimensional (2-D) real cepstrum. This novel 2-D cepstrum method is compared with principal component analysis (PCA) method by testing over the MSTAR image database. The extracted features are classified using Support Vector Machine (SVM). We demonstrate that discrimination of natural background (clutter) and man-made objects (metal objects) in SAR imagery is possible using the 2-D cepstrum feature parameters. In addition, the computational cost of the cepstrum method is lower than the PCA method. Experimental results are presented. ©2009 IEEE.
Keywords
CepstrumCepstrum method
Computational costs
Feature parameters
Image database
Man made objects
Natural backgrounds
PCA method
SAR imagery
SAR Images
Synthetic aperture radar images
Feature extraction
Image retrieval
Imaging systems
Metal recovery
Object recognition
Parameter extraction
Principal component analysis
Smelting
Support vector machines
Synthetic apertures
Synthetic aperture radar